Linear Regression ML Boston.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.datasets import load_boston\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rahul-raoniar/miniconda3/envs/datascience/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n",
      "\n",
      "    The Boston housing prices dataset has an ethical problem. You can refer to\n",
      "    the documentation of this function for further details.\n",
      "\n",
      "    The scikit-learn maintainers therefore strongly discourage the use of this\n",
      "    dataset unless the purpose of the code is to study and educate about\n",
      "    ethical issues in data science and machine learning.\n",
      "\n",
      "    In this special case, you can fetch the dataset from the original\n",
      "    source::\n",
      "\n",
      "        import pandas as pd\n",
      "        import numpy as np\n",
      "\n",
      "        data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
      "        raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
      "        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
      "        target = raw_df.values[1::2, 2]\n",
      "\n",
      "    Alternative datasets include the California housing dataset (i.e.\n",
      "    :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n",
      "    dataset. You can load the datasets as follows::\n",
      "\n",
      "        from sklearn.datasets import fetch_california_housing\n",
      "        housing = fetch_california_housing()\n",
      "\n",
      "    for the California housing dataset and::\n",
      "\n",
      "        from sklearn.datasets import fetch_openml\n",
      "        housing = fetch_openml(name=\"house_prices\", as_frame=True)\n",
      "\n",
      "    for the Ames housing dataset.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "boston = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename', 'data_module'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _boston_dataset:\n",
      "\n",
      "Boston house prices dataset\n",
      "---------------------------\n",
      "\n",
      "**Data Set Characteristics:**  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n",
      "\n",
      "    :Attribute Information (in order):\n",
      "        - CRIM     per capita crime rate by town\n",
      "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
      "        - INDUS    proportion of non-retail business acres per town\n",
      "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
      "        - NOX      nitric oxides concentration (parts per 10 million)\n",
      "        - RM       average number of rooms per dwelling\n",
      "        - AGE      proportion of owner-occupied units built prior to 1940\n",
      "        - DIS      weighted distances to five Boston employment centres\n",
      "        - RAD      index of accessibility to radial highways\n",
      "        - TAX      full-value property-tax rate per $10,000\n",
      "        - PTRATIO  pupil-teacher ratio by town\n",
      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of black people by town\n",
      "        - LSTAT    % lower status of the population\n",
      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      ".. topic:: References\n",
      "\n",
      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check description of the dataset\n",
    "\n",
    "print(boston.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6.3200e-03 1.8000e+01 2.3100e+00 ... 1.5300e+01 3.9690e+02 4.9800e+00]\n",
      " [2.7310e-02 0.0000e+00 7.0700e+00 ... 1.7800e+01 3.9690e+02 9.1400e+00]\n",
      " [2.7290e-02 0.0000e+00 7.0700e+00 ... 1.7800e+01 3.9283e+02 4.0300e+00]\n",
      " ...\n",
      " [6.0760e-02 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9690e+02 5.6400e+00]\n",
      " [1.0959e-01 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9345e+02 6.4800e+00]\n",
      " [4.7410e-02 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9690e+02 7.8800e+00]]\n"
     ]
    }
   ],
   "source": [
    "print(boston.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[24.  21.6 34.7 33.4 36.2 28.7 22.9 27.1 16.5 18.9 15.  18.9 21.7 20.4\n",
      " 18.2 19.9 23.1 17.5 20.2 18.2 13.6 19.6 15.2 14.5 15.6 13.9 16.6 14.8\n",
      " 18.4 21.  12.7 14.5 13.2 13.1 13.5 18.9 20.  21.  24.7 30.8 34.9 26.6\n",
      " 25.3 24.7 21.2 19.3 20.  16.6 14.4 19.4 19.7 20.5 25.  23.4 18.9 35.4\n",
      " 24.7 31.6 23.3 19.6 18.7 16.  22.2 25.  33.  23.5 19.4 22.  17.4 20.9\n",
      " 24.2 21.7 22.8 23.4 24.1 21.4 20.  20.8 21.2 20.3 28.  23.9 24.8 22.9\n",
      " 23.9 26.6 22.5 22.2 23.6 28.7 22.6 22.  22.9 25.  20.6 28.4 21.4 38.7\n",
      " 43.8 33.2 27.5 26.5 18.6 19.3 20.1 19.5 19.5 20.4 19.8 19.4 21.7 22.8\n",
      " 18.8 18.7 18.5 18.3 21.2 19.2 20.4 19.3 22.  20.3 20.5 17.3 18.8 21.4\n",
      " 15.7 16.2 18.  14.3 19.2 19.6 23.  18.4 15.6 18.1 17.4 17.1 13.3 17.8\n",
      " 14.  14.4 13.4 15.6 11.8 13.8 15.6 14.6 17.8 15.4 21.5 19.6 15.3 19.4\n",
      " 17.  15.6 13.1 41.3 24.3 23.3 27.  50.  50.  50.  22.7 25.  50.  23.8\n",
      " 23.8 22.3 17.4 19.1 23.1 23.6 22.6 29.4 23.2 24.6 29.9 37.2 39.8 36.2\n",
      " 37.9 32.5 26.4 29.6 50.  32.  29.8 34.9 37.  30.5 36.4 31.1 29.1 50.\n",
      " 33.3 30.3 34.6 34.9 32.9 24.1 42.3 48.5 50.  22.6 24.4 22.5 24.4 20.\n",
      " 21.7 19.3 22.4 28.1 23.7 25.  23.3 28.7 21.5 23.  26.7 21.7 27.5 30.1\n",
      " 44.8 50.  37.6 31.6 46.7 31.5 24.3 31.7 41.7 48.3 29.  24.  25.1 31.5\n",
      " 23.7 23.3 22.  20.1 22.2 23.7 17.6 18.5 24.3 20.5 24.5 26.2 24.4 24.8\n",
      " 29.6 42.8 21.9 20.9 44.  50.  36.  30.1 33.8 43.1 48.8 31.  36.5 22.8\n",
      " 30.7 50.  43.5 20.7 21.1 25.2 24.4 35.2 32.4 32.  33.2 33.1 29.1 35.1\n",
      " 45.4 35.4 46.  50.  32.2 22.  20.1 23.2 22.3 24.8 28.5 37.3 27.9 23.9\n",
      " 21.7 28.6 27.1 20.3 22.5 29.  24.8 22.  26.4 33.1 36.1 28.4 33.4 28.2\n",
      " 22.8 20.3 16.1 22.1 19.4 21.6 23.8 16.2 17.8 19.8 23.1 21.  23.8 23.1\n",
      " 20.4 18.5 25.  24.6 23.  22.2 19.3 22.6 19.8 17.1 19.4 22.2 20.7 21.1\n",
      " 19.5 18.5 20.6 19.  18.7 32.7 16.5 23.9 31.2 17.5 17.2 23.1 24.5 26.6\n",
      " 22.9 24.1 18.6 30.1 18.2 20.6 17.8 21.7 22.7 22.6 25.  19.9 20.8 16.8\n",
      " 21.9 27.5 21.9 23.1 50.  50.  50.  50.  50.  13.8 13.8 15.  13.9 13.3\n",
      " 13.1 10.2 10.4 10.9 11.3 12.3  8.8  7.2 10.5  7.4 10.2 11.5 15.1 23.2\n",
      "  9.7 13.8 12.7 13.1 12.5  8.5  5.   6.3  5.6  7.2 12.1  8.3  8.5  5.\n",
      " 11.9 27.9 17.2 27.5 15.  17.2 17.9 16.3  7.   7.2  7.5 10.4  8.8  8.4\n",
      " 16.7 14.2 20.8 13.4 11.7  8.3 10.2 10.9 11.   9.5 14.5 14.1 16.1 14.3\n",
      " 11.7 13.4  9.6  8.7  8.4 12.8 10.5 17.1 18.4 15.4 10.8 11.8 14.9 12.6\n",
      " 14.1 13.  13.4 15.2 16.1 17.8 14.9 14.1 12.7 13.5 14.9 20.  16.4 17.7\n",
      " 19.5 20.2 21.4 19.9 19.  19.1 19.1 20.1 19.9 19.6 23.2 29.8 13.8 13.3\n",
      " 16.7 12.  14.6 21.4 23.  23.7 25.  21.8 20.6 21.2 19.1 20.6 15.2  7.\n",
      "  8.1 13.6 20.1 21.8 24.5 23.1 19.7 18.3 21.2 17.5 16.8 22.4 20.6 23.9\n",
      " 22.  11.9]\n"
     ]
    }
   ],
   "source": [
    "print(boston.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['CRIM' 'ZN' 'INDUS' 'CHAS' 'NOX' 'RM' 'AGE' 'DIS' 'RAD' 'TAX' 'PTRATIO'\n",
      " 'B' 'LSTAT']\n"
     ]
    }
   ],
   "source": [
    "print(boston.feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
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       "      <th>LSTAT</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  \n",
       "0     15.3  396.90   4.98  \n",
       "1     17.8  396.90   9.14  \n",
       "2     17.8  392.83   4.03  \n",
       "3     18.7  394.63   2.94  \n",
       "4     18.7  396.90   5.33  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.DataFrame(boston.data, columns = boston.feature_names)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[\"Price\"] = boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  Price  \n",
       "0     15.3  396.90   4.98   24.0  \n",
       "1     17.8  396.90   9.14   21.6  \n",
       "2     17.8  392.83   4.03   34.7  \n",
       "3     18.7  394.63   2.94   33.4  \n",
       "4     18.7  396.90   5.33   36.2  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   CRIM     506 non-null    float64\n",
      " 1   ZN       506 non-null    float64\n",
      " 2   INDUS    506 non-null    float64\n",
      " 3   CHAS     506 non-null    float64\n",
      " 4   NOX      506 non-null    float64\n",
      " 5   RM       506 non-null    float64\n",
      " 6   AGE      506 non-null    float64\n",
      " 7   DIS      506 non-null    float64\n",
      " 8   RAD      506 non-null    float64\n",
      " 9   TAX      506 non-null    float64\n",
      " 10  PTRATIO  506 non-null    float64\n",
      " 11  B        506 non-null    float64\n",
      " 12  LSTAT    506 non-null    float64\n",
      " 13  Price    506 non-null    float64\n",
      "dtypes: float64(14)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CRIM</th>\n",
       "      <td>506.0</td>\n",
       "      <td>3.613524</td>\n",
       "      <td>8.601545</td>\n",
       "      <td>0.00632</td>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.25651</td>\n",
       "      <td>3.677083</td>\n",
       "      <td>88.9762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN</th>\n",
       "      <td>506.0</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS</th>\n",
       "      <td>506.0</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.46000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>9.69000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>27.7400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAS</th>\n",
       "      <td>506.0</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOX</th>\n",
       "      <td>506.0</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.38500</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>0.53800</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>0.8710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM</th>\n",
       "      <td>506.0</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>3.56100</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>6.20850</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>8.7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>506.0</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.90000</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>77.50000</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS</th>\n",
       "      <td>506.0</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>1.12960</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>3.20745</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>12.1265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD</th>\n",
       "      <td>506.0</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>24.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAX</th>\n",
       "      <td>506.0</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>187.00000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>330.00000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>711.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTRATIO</th>\n",
       "      <td>506.0</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>12.60000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>19.05000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>22.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>506.0</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>0.32000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>391.44000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>396.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTAT</th>\n",
       "      <td>506.0</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>1.73000</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>11.36000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>37.9700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>506.0</td>\n",
       "      <td>22.532806</td>\n",
       "      <td>9.197104</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>17.025000</td>\n",
       "      <td>21.20000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>50.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count        mean         std        min         25%        50%  \\\n",
       "CRIM     506.0    3.613524    8.601545    0.00632    0.082045    0.25651   \n",
       "ZN       506.0   11.363636   23.322453    0.00000    0.000000    0.00000   \n",
       "INDUS    506.0   11.136779    6.860353    0.46000    5.190000    9.69000   \n",
       "CHAS     506.0    0.069170    0.253994    0.00000    0.000000    0.00000   \n",
       "NOX      506.0    0.554695    0.115878    0.38500    0.449000    0.53800   \n",
       "RM       506.0    6.284634    0.702617    3.56100    5.885500    6.20850   \n",
       "AGE      506.0   68.574901   28.148861    2.90000   45.025000   77.50000   \n",
       "DIS      506.0    3.795043    2.105710    1.12960    2.100175    3.20745   \n",
       "RAD      506.0    9.549407    8.707259    1.00000    4.000000    5.00000   \n",
       "TAX      506.0  408.237154  168.537116  187.00000  279.000000  330.00000   \n",
       "PTRATIO  506.0   18.455534    2.164946   12.60000   17.400000   19.05000   \n",
       "B        506.0  356.674032   91.294864    0.32000  375.377500  391.44000   \n",
       "LSTAT    506.0   12.653063    7.141062    1.73000    6.950000   11.36000   \n",
       "Price    506.0   22.532806    9.197104    5.00000   17.025000   21.20000   \n",
       "\n",
       "                75%       max  \n",
       "CRIM       3.677083   88.9762  \n",
       "ZN        12.500000  100.0000  \n",
       "INDUS     18.100000   27.7400  \n",
       "CHAS       0.000000    1.0000  \n",
       "NOX        0.624000    0.8710  \n",
       "RM         6.623500    8.7800  \n",
       "AGE       94.075000  100.0000  \n",
       "DIS        5.188425   12.1265  \n",
       "RAD       24.000000   24.0000  \n",
       "TAX      666.000000  711.0000  \n",
       "PTRATIO   20.200000   22.0000  \n",
       "B        396.225000  396.9000  \n",
       "LSTAT     16.955000   37.9700  \n",
       "Price     25.000000   50.0000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       0\n",
       "ZN         0\n",
       "INDUS      0\n",
       "CHAS       0\n",
       "NOX        0\n",
       "RM         0\n",
       "AGE        0\n",
       "DIS        0\n",
       "RAD        0\n",
       "TAX        0\n",
       "PTRATIO    0\n",
       "B          0\n",
       "LSTAT      0\n",
       "Price      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check missing values\n",
    "\n",
    "dataset.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
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       "      <th>TAX</th>\n",
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       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CRIM</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.200469</td>\n",
       "      <td>0.406583</td>\n",
       "      <td>-0.055892</td>\n",
       "      <td>0.420972</td>\n",
       "      <td>-0.219247</td>\n",
       "      <td>0.352734</td>\n",
       "      <td>-0.379670</td>\n",
       "      <td>0.625505</td>\n",
       "      <td>0.582764</td>\n",
       "      <td>0.289946</td>\n",
       "      <td>-0.385064</td>\n",
       "      <td>0.455621</td>\n",
       "      <td>-0.388305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN</th>\n",
       "      <td>-0.200469</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.533828</td>\n",
       "      <td>-0.042697</td>\n",
       "      <td>-0.516604</td>\n",
       "      <td>0.311991</td>\n",
       "      <td>-0.569537</td>\n",
       "      <td>0.664408</td>\n",
       "      <td>-0.311948</td>\n",
       "      <td>-0.314563</td>\n",
       "      <td>-0.391679</td>\n",
       "      <td>0.175520</td>\n",
       "      <td>-0.412995</td>\n",
       "      <td>0.360445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS</th>\n",
       "      <td>0.406583</td>\n",
       "      <td>-0.533828</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062938</td>\n",
       "      <td>0.763651</td>\n",
       "      <td>-0.391676</td>\n",
       "      <td>0.644779</td>\n",
       "      <td>-0.708027</td>\n",
       "      <td>0.595129</td>\n",
       "      <td>0.720760</td>\n",
       "      <td>0.383248</td>\n",
       "      <td>-0.356977</td>\n",
       "      <td>0.603800</td>\n",
       "      <td>-0.483725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAS</th>\n",
       "      <td>-0.055892</td>\n",
       "      <td>-0.042697</td>\n",
       "      <td>0.062938</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.091203</td>\n",
       "      <td>0.091251</td>\n",
       "      <td>0.086518</td>\n",
       "      <td>-0.099176</td>\n",
       "      <td>-0.007368</td>\n",
       "      <td>-0.035587</td>\n",
       "      <td>-0.121515</td>\n",
       "      <td>0.048788</td>\n",
       "      <td>-0.053929</td>\n",
       "      <td>0.175260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOX</th>\n",
       "      <td>0.420972</td>\n",
       "      <td>-0.516604</td>\n",
       "      <td>0.763651</td>\n",
       "      <td>0.091203</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.302188</td>\n",
       "      <td>0.731470</td>\n",
       "      <td>-0.769230</td>\n",
       "      <td>0.611441</td>\n",
       "      <td>0.668023</td>\n",
       "      <td>0.188933</td>\n",
       "      <td>-0.380051</td>\n",
       "      <td>0.590879</td>\n",
       "      <td>-0.427321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM</th>\n",
       "      <td>-0.219247</td>\n",
       "      <td>0.311991</td>\n",
       "      <td>-0.391676</td>\n",
       "      <td>0.091251</td>\n",
       "      <td>-0.302188</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.240265</td>\n",
       "      <td>0.205246</td>\n",
       "      <td>-0.209847</td>\n",
       "      <td>-0.292048</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>0.128069</td>\n",
       "      <td>-0.613808</td>\n",
       "      <td>0.695360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>0.352734</td>\n",
       "      <td>-0.569537</td>\n",
       "      <td>0.644779</td>\n",
       "      <td>0.086518</td>\n",
       "      <td>0.731470</td>\n",
       "      <td>-0.240265</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.747881</td>\n",
       "      <td>0.456022</td>\n",
       "      <td>0.506456</td>\n",
       "      <td>0.261515</td>\n",
       "      <td>-0.273534</td>\n",
       "      <td>0.602339</td>\n",
       "      <td>-0.376955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS</th>\n",
       "      <td>-0.379670</td>\n",
       "      <td>0.664408</td>\n",
       "      <td>-0.708027</td>\n",
       "      <td>-0.099176</td>\n",
       "      <td>-0.769230</td>\n",
       "      <td>0.205246</td>\n",
       "      <td>-0.747881</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.494588</td>\n",
       "      <td>-0.534432</td>\n",
       "      <td>-0.232471</td>\n",
       "      <td>0.291512</td>\n",
       "      <td>-0.496996</td>\n",
       "      <td>0.249929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD</th>\n",
       "      <td>0.625505</td>\n",
       "      <td>-0.311948</td>\n",
       "      <td>0.595129</td>\n",
       "      <td>-0.007368</td>\n",
       "      <td>0.611441</td>\n",
       "      <td>-0.209847</td>\n",
       "      <td>0.456022</td>\n",
       "      <td>-0.494588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.910228</td>\n",
       "      <td>0.464741</td>\n",
       "      <td>-0.444413</td>\n",
       "      <td>0.488676</td>\n",
       "      <td>-0.381626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAX</th>\n",
       "      <td>0.582764</td>\n",
       "      <td>-0.314563</td>\n",
       "      <td>0.720760</td>\n",
       "      <td>-0.035587</td>\n",
       "      <td>0.668023</td>\n",
       "      <td>-0.292048</td>\n",
       "      <td>0.506456</td>\n",
       "      <td>-0.534432</td>\n",
       "      <td>0.910228</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.460853</td>\n",
       "      <td>-0.441808</td>\n",
       "      <td>0.543993</td>\n",
       "      <td>-0.468536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTRATIO</th>\n",
       "      <td>0.289946</td>\n",
       "      <td>-0.391679</td>\n",
       "      <td>0.383248</td>\n",
       "      <td>-0.121515</td>\n",
       "      <td>0.188933</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>0.261515</td>\n",
       "      <td>-0.232471</td>\n",
       "      <td>0.464741</td>\n",
       "      <td>0.460853</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.177383</td>\n",
       "      <td>0.374044</td>\n",
       "      <td>-0.507787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.385064</td>\n",
       "      <td>0.175520</td>\n",
       "      <td>-0.356977</td>\n",
       "      <td>0.048788</td>\n",
       "      <td>-0.380051</td>\n",
       "      <td>0.128069</td>\n",
       "      <td>-0.273534</td>\n",
       "      <td>0.291512</td>\n",
       "      <td>-0.444413</td>\n",
       "      <td>-0.441808</td>\n",
       "      <td>-0.177383</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.366087</td>\n",
       "      <td>0.333461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTAT</th>\n",
       "      <td>0.455621</td>\n",
       "      <td>-0.412995</td>\n",
       "      <td>0.603800</td>\n",
       "      <td>-0.053929</td>\n",
       "      <td>0.590879</td>\n",
       "      <td>-0.613808</td>\n",
       "      <td>0.602339</td>\n",
       "      <td>-0.496996</td>\n",
       "      <td>0.488676</td>\n",
       "      <td>0.543993</td>\n",
       "      <td>0.374044</td>\n",
       "      <td>-0.366087</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.737663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>-0.388305</td>\n",
       "      <td>0.360445</td>\n",
       "      <td>-0.483725</td>\n",
       "      <td>0.175260</td>\n",
       "      <td>-0.427321</td>\n",
       "      <td>0.695360</td>\n",
       "      <td>-0.376955</td>\n",
       "      <td>0.249929</td>\n",
       "      <td>-0.381626</td>\n",
       "      <td>-0.468536</td>\n",
       "      <td>-0.507787</td>\n",
       "      <td>0.333461</td>\n",
       "      <td>-0.737663</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM        ZN     INDUS      CHAS       NOX        RM       AGE  \\\n",
       "CRIM     1.000000 -0.200469  0.406583 -0.055892  0.420972 -0.219247  0.352734   \n",
       "ZN      -0.200469  1.000000 -0.533828 -0.042697 -0.516604  0.311991 -0.569537   \n",
       "INDUS    0.406583 -0.533828  1.000000  0.062938  0.763651 -0.391676  0.644779   \n",
       "CHAS    -0.055892 -0.042697  0.062938  1.000000  0.091203  0.091251  0.086518   \n",
       "NOX      0.420972 -0.516604  0.763651  0.091203  1.000000 -0.302188  0.731470   \n",
       "RM      -0.219247  0.311991 -0.391676  0.091251 -0.302188  1.000000 -0.240265   \n",
       "AGE      0.352734 -0.569537  0.644779  0.086518  0.731470 -0.240265  1.000000   \n",
       "DIS     -0.379670  0.664408 -0.708027 -0.099176 -0.769230  0.205246 -0.747881   \n",
       "RAD      0.625505 -0.311948  0.595129 -0.007368  0.611441 -0.209847  0.456022   \n",
       "TAX      0.582764 -0.314563  0.720760 -0.035587  0.668023 -0.292048  0.506456   \n",
       "PTRATIO  0.289946 -0.391679  0.383248 -0.121515  0.188933 -0.355501  0.261515   \n",
       "B       -0.385064  0.175520 -0.356977  0.048788 -0.380051  0.128069 -0.273534   \n",
       "LSTAT    0.455621 -0.412995  0.603800 -0.053929  0.590879 -0.613808  0.602339   \n",
       "Price   -0.388305  0.360445 -0.483725  0.175260 -0.427321  0.695360 -0.376955   \n",
       "\n",
       "              DIS       RAD       TAX   PTRATIO         B     LSTAT     Price  \n",
       "CRIM    -0.379670  0.625505  0.582764  0.289946 -0.385064  0.455621 -0.388305  \n",
       "ZN       0.664408 -0.311948 -0.314563 -0.391679  0.175520 -0.412995  0.360445  \n",
       "INDUS   -0.708027  0.595129  0.720760  0.383248 -0.356977  0.603800 -0.483725  \n",
       "CHAS    -0.099176 -0.007368 -0.035587 -0.121515  0.048788 -0.053929  0.175260  \n",
       "NOX     -0.769230  0.611441  0.668023  0.188933 -0.380051  0.590879 -0.427321  \n",
       "RM       0.205246 -0.209847 -0.292048 -0.355501  0.128069 -0.613808  0.695360  \n",
       "AGE     -0.747881  0.456022  0.506456  0.261515 -0.273534  0.602339 -0.376955  \n",
       "DIS      1.000000 -0.494588 -0.534432 -0.232471  0.291512 -0.496996  0.249929  \n",
       "RAD     -0.494588  1.000000  0.910228  0.464741 -0.444413  0.488676 -0.381626  \n",
       "TAX     -0.534432  0.910228  1.000000  0.460853 -0.441808  0.543993 -0.468536  \n",
       "PTRATIO -0.232471  0.464741  0.460853  1.000000 -0.177383  0.374044 -0.507787  \n",
       "B        0.291512 -0.444413 -0.441808 -0.177383  1.000000 -0.366087  0.333461  \n",
       "LSTAT   -0.496996  0.488676  0.543993  0.374044 -0.366087  1.000000 -0.737663  \n",
       "Price    0.249929 -0.381626 -0.468536 -0.507787  0.333461 -0.737663  1.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# EDA\n",
    "\n",
    "# Correlation\n",
    "\n",
    "dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.pairplot(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8, 4))\n",
    "\n",
    "ax.scatter(dataset[\"CRIM\"],\n",
    "        dataset[\"Price\"])\n",
    "ax.set_xlabel(\"CRIM\")\n",
    "ax.set_ylabel(\"Price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8, 4))\n",
    "\n",
    "ax.scatter(dataset[\"RM\"],\n",
    "        dataset[\"Price\"])\n",
    "ax.set_xlabel(\"RM\")\n",
    "ax.set_ylabel(\"Price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fe6a6fa18b0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x = \"RM\",\n",
    "        y = \"Price\",\n",
    "        data = dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fe68db23eb0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x = \"LSTAT\",\n",
    "        y = \"Price\",\n",
    "        data = dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preparation for Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Dividing dataset into independent variable and dependent variable\n",
    "\n",
    "X = dataset.drop(\"Price\", axis = 1)\n",
    "y = dataset['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',\n",
       "       'PTRATIO', 'B', 'LSTAT'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train Test Split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "                    X,\n",
    "                    y,\n",
    "                    test_size = 0.3,\n",
    "                    random_state = 42\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.02985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.430</td>\n",
       "      <td>58.7</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.12</td>\n",
       "      <td>5.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>0.13158</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.547</td>\n",
       "      <td>6.176</td>\n",
       "      <td>72.5</td>\n",
       "      <td>2.7301</td>\n",
       "      <td>6.0</td>\n",
       "      <td>432.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>393.30</td>\n",
       "      <td>12.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.17142</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.91</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.448</td>\n",
       "      <td>5.682</td>\n",
       "      <td>33.8</td>\n",
       "      <td>5.1004</td>\n",
       "      <td>3.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>396.90</td>\n",
       "      <td>10.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.05393</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>5.935</td>\n",
       "      <td>29.3</td>\n",
       "      <td>4.4986</td>\n",
       "      <td>4.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>386.85</td>\n",
       "      <td>6.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>15.57570</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.580</td>\n",
       "      <td>5.926</td>\n",
       "      <td>71.0</td>\n",
       "      <td>2.9084</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>368.74</td>\n",
       "      <td>18.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>0.17120</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.520</td>\n",
       "      <td>5.836</td>\n",
       "      <td>91.9</td>\n",
       "      <td>2.2110</td>\n",
       "      <td>5.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>20.9</td>\n",
       "      <td>395.67</td>\n",
       "      <td>18.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>0.29916</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.464</td>\n",
       "      <td>5.856</td>\n",
       "      <td>42.1</td>\n",
       "      <td>4.4290</td>\n",
       "      <td>3.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>388.65</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>0.01501</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.435</td>\n",
       "      <td>6.635</td>\n",
       "      <td>29.7</td>\n",
       "      <td>8.3440</td>\n",
       "      <td>4.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>390.94</td>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>11.16040</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.740</td>\n",
       "      <td>6.629</td>\n",
       "      <td>94.6</td>\n",
       "      <td>2.1247</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>109.85</td>\n",
       "      <td>23.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>0.22876</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.520</td>\n",
       "      <td>6.405</td>\n",
       "      <td>85.4</td>\n",
       "      <td>2.7147</td>\n",
       "      <td>5.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>20.9</td>\n",
       "      <td>70.80</td>\n",
       "      <td>10.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>354 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS   RAD    TAX  \\\n",
       "5     0.02985   0.0   2.18   0.0  0.458  6.430  58.7  6.0622   3.0  222.0   \n",
       "116   0.13158   0.0  10.01   0.0  0.547  6.176  72.5  2.7301   6.0  432.0   \n",
       "45    0.17142   0.0   6.91   0.0  0.448  5.682  33.8  5.1004   3.0  233.0   \n",
       "16    1.05393   0.0   8.14   0.0  0.538  5.935  29.3  4.4986   4.0  307.0   \n",
       "468  15.57570   0.0  18.10   0.0  0.580  5.926  71.0  2.9084  24.0  666.0   \n",
       "..        ...   ...    ...   ...    ...    ...   ...     ...   ...    ...   \n",
       "106   0.17120   0.0   8.56   0.0  0.520  5.836  91.9  2.2110   5.0  384.0   \n",
       "270   0.29916  20.0   6.96   0.0  0.464  5.856  42.1  4.4290   3.0  223.0   \n",
       "348   0.01501  80.0   2.01   0.0  0.435  6.635  29.7  8.3440   4.0  280.0   \n",
       "435  11.16040   0.0  18.10   0.0  0.740  6.629  94.6  2.1247  24.0  666.0   \n",
       "102   0.22876   0.0   8.56   0.0  0.520  6.405  85.4  2.7147   5.0  384.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  \n",
       "5       18.7  394.12   5.21  \n",
       "116     17.8  393.30  12.04  \n",
       "45      17.9  396.90  10.21  \n",
       "16      21.0  386.85   6.58  \n",
       "468     20.2  368.74  18.13  \n",
       "..       ...     ...    ...  \n",
       "106     20.9  395.67  18.66  \n",
       "270     18.6  388.65  13.00  \n",
       "348     17.0  390.94   5.99  \n",
       "435     20.2  109.85  23.27  \n",
       "102     20.9   70.80  10.63  \n",
       "\n",
       "[354 rows x 13 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>0.09178</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.510</td>\n",
       "      <td>6.416</td>\n",
       "      <td>84.1</td>\n",
       "      <td>2.6463</td>\n",
       "      <td>5.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>16.6</td>\n",
       "      <td>395.50</td>\n",
       "      <td>9.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>0.05644</td>\n",
       "      <td>40.0</td>\n",
       "      <td>6.41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.447</td>\n",
       "      <td>6.758</td>\n",
       "      <td>32.9</td>\n",
       "      <td>4.0776</td>\n",
       "      <td>4.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>396.90</td>\n",
       "      <td>3.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>0.10574</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.609</td>\n",
       "      <td>5.983</td>\n",
       "      <td>98.8</td>\n",
       "      <td>1.8681</td>\n",
       "      <td>4.0</td>\n",
       "      <td>711.0</td>\n",
       "      <td>20.1</td>\n",
       "      <td>390.11</td>\n",
       "      <td>18.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0.09164</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.413</td>\n",
       "      <td>6.065</td>\n",
       "      <td>7.8</td>\n",
       "      <td>5.2873</td>\n",
       "      <td>4.0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>19.2</td>\n",
       "      <td>390.91</td>\n",
       "      <td>5.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>5.09017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.713</td>\n",
       "      <td>6.297</td>\n",
       "      <td>91.8</td>\n",
       "      <td>2.3682</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>385.09</td>\n",
       "      <td>17.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>9.72418</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.740</td>\n",
       "      <td>6.406</td>\n",
       "      <td>97.2</td>\n",
       "      <td>2.0651</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>385.96</td>\n",
       "      <td>19.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.98843</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>5.813</td>\n",
       "      <td>100.0</td>\n",
       "      <td>4.0952</td>\n",
       "      <td>4.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>394.54</td>\n",
       "      <td>19.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>0.52693</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.504</td>\n",
       "      <td>8.725</td>\n",
       "      <td>83.0</td>\n",
       "      <td>2.8944</td>\n",
       "      <td>8.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>382.00</td>\n",
       "      <td>4.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>5.58107</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.713</td>\n",
       "      <td>6.436</td>\n",
       "      <td>87.9</td>\n",
       "      <td>2.3158</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>100.19</td>\n",
       "      <td>16.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>9.92485</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.740</td>\n",
       "      <td>6.251</td>\n",
       "      <td>96.6</td>\n",
       "      <td>2.1980</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>388.52</td>\n",
       "      <td>16.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>152 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM    AGE     DIS   RAD    TAX  \\\n",
       "173  0.09178   0.0   4.05   0.0  0.510  6.416   84.1  2.6463   5.0  296.0   \n",
       "274  0.05644  40.0   6.41   1.0  0.447  6.758   32.9  4.0776   4.0  254.0   \n",
       "491  0.10574   0.0  27.74   0.0  0.609  5.983   98.8  1.8681   4.0  711.0   \n",
       "72   0.09164   0.0  10.81   0.0  0.413  6.065    7.8  5.2873   4.0  305.0   \n",
       "452  5.09017   0.0  18.10   0.0  0.713  6.297   91.8  2.3682  24.0  666.0   \n",
       "..       ...   ...    ...   ...    ...    ...    ...     ...   ...    ...   \n",
       "441  9.72418   0.0  18.10   0.0  0.740  6.406   97.2  2.0651  24.0  666.0   \n",
       "23   0.98843   0.0   8.14   0.0  0.538  5.813  100.0  4.0952   4.0  307.0   \n",
       "225  0.52693   0.0   6.20   0.0  0.504  8.725   83.0  2.8944   8.0  307.0   \n",
       "433  5.58107   0.0  18.10   0.0  0.713  6.436   87.9  2.3158  24.0  666.0   \n",
       "447  9.92485   0.0  18.10   0.0  0.740  6.251   96.6  2.1980  24.0  666.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  \n",
       "173     16.6  395.50   9.04  \n",
       "274     17.6  396.90   3.53  \n",
       "491     20.1  390.11  18.07  \n",
       "72      19.2  390.91   5.52  \n",
       "452     20.2  385.09  17.27  \n",
       "..       ...     ...    ...  \n",
       "441     20.2  385.96  19.52  \n",
       "23      21.0  394.54  19.88  \n",
       "225     17.4  382.00   4.63  \n",
       "433     20.2  100.19  16.22  \n",
       "447     20.2  388.52  16.44  \n",
       "\n",
       "[152 rows x 13 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardizing datasets\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.41425879, -0.50512499, -1.29214218, ...,  0.18727079,\n",
       "         0.39651419, -1.01531611],\n",
       "       [-0.40200818, -0.50512499, -0.16208345, ..., -0.21208981,\n",
       "         0.3870674 , -0.05366252],\n",
       "       [-0.39721053, -0.50512499, -0.60948856, ..., -0.16771641,\n",
       "         0.42854113, -0.31132373],\n",
       "       ...,\n",
       "       [-0.41604586,  3.03838247, -1.3166773 , ..., -0.56707702,\n",
       "         0.35987906, -0.90549329],\n",
       "       [ 0.92611293, -0.50512499,  1.00549958, ...,  0.8528718 ,\n",
       "        -2.87841346,  1.52750437],\n",
       "       [-0.39030549, -0.50512499, -0.37135358, ...,  1.16348561,\n",
       "        -3.32828832, -0.25218837]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(scaler, open('scaling.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regression = LinearRegression()\n",
    "regression.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.10834602,  0.80843998,  0.34313466,  0.81386426, -1.79804295,\n",
       "        2.913858  , -0.29893918, -2.94251148,  2.09419303, -1.44706731,\n",
       "       -2.05232232,  1.02375187, -3.88579002])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print coefs.\n",
    "\n",
    "regression.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Var_name</th>\n",
       "      <th>Coef.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CRIM</td>\n",
       "      <td>-1.108346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZN</td>\n",
       "      <td>0.808440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>INDUS</td>\n",
       "      <td>0.343135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHAS</td>\n",
       "      <td>0.813864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NOX</td>\n",
       "      <td>-1.798043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RM</td>\n",
       "      <td>2.913858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AGE</td>\n",
       "      <td>-0.298939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DIS</td>\n",
       "      <td>-2.942511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RAD</td>\n",
       "      <td>2.094193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TAX</td>\n",
       "      <td>-1.447067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PTRATIO</td>\n",
       "      <td>-2.052322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>B</td>\n",
       "      <td>1.023752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LSTAT</td>\n",
       "      <td>-3.885790</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Var_name     Coef.\n",
       "0      CRIM -1.108346\n",
       "1        ZN  0.808440\n",
       "2     INDUS  0.343135\n",
       "3      CHAS  0.813864\n",
       "4       NOX -1.798043\n",
       "5        RM  2.913858\n",
       "6       AGE -0.298939\n",
       "7       DIS -2.942511\n",
       "8       RAD  2.094193\n",
       "9       TAX -1.447067\n",
       "10  PTRATIO -2.052322\n",
       "11        B  1.023752\n",
       "12    LSTAT -3.885790"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefs = pd.DataFrame({\"Var_name\": list(X.columns),\n",
    "            \"Coef.\": list(regression.coef_)})\n",
    "coefs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([28.64896005, 36.49501384, 15.4111932 , 25.40321303, 18.85527988,\n",
       "       23.14668944, 17.3921241 , 14.07859899, 23.03692679, 20.59943345])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction with test dataset\n",
    "\n",
    "reg_pred = regression.predict(X_test)\n",
    "reg_pred[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### # Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Predicted')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8, 4))\n",
    "\n",
    "ax.scatter(y_test,\n",
    "           reg_pred)\n",
    "ax.set_xlabel(\"Actual\")\n",
    "ax.set_ylabel(\"Predicted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f2cee1896a0>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Residuals \n",
    "\n",
    "residuals = y_test - reg_pred\n",
    "sns.displot(residuals, kind = \"kde\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Residuals')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Scatter plot of residuals and predictions\n",
    "fig, ax = plt.subplots(figsize = (8, 4))\n",
    "\n",
    "ax.scatter(reg_pred,\n",
    "           residuals)\n",
    "ax.set_xlabel(\"Predictions\")\n",
    "ax.set_ylabel(\"Residuals\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21.5174442311772\n",
      "3.162709871457405\n",
      "4.63868992617282\n"
     ]
    }
   ],
   "source": [
    "# Model evaluation\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "\n",
    "\n",
    "print(mean_squared_error(y_test, reg_pred))\n",
    "print(mean_absolute_error(y_test, reg_pred))\n",
    "print(np.sqrt(mean_squared_error(y_test, reg_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$R^2 = 1-\\frac{SSR}{SST}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7112260057484934\n"
     ]
    }
   ],
   "source": [
    "# R-square and Adjusted R-square\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "score = r2_score(y_test, reg_pred)\n",
    "print(score)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$Adj\\, R^2 = 1- \\frac{(1-R^2)(n-1)}{(n-k-1)}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6840226584639312"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_adj = 1 - (1-score)*(len(y_test)-1)/(len(y_test) - X_test.shape[1]-1)\n",
    "r2_adj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### New Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.320e-03, 1.800e+01, 2.310e+00, 0.000e+00, 5.380e-01, 6.575e+00,\n",
       "       6.520e+01, 4.090e+00, 1.000e+00, 2.960e+02, 1.530e+01, 3.969e+02,\n",
       "       4.980e+00])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13,)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.data[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.320e-03, 1.800e+01, 2.310e+00, 0.000e+00, 5.380e-01, 6.575e+00,\n",
       "        6.520e+01, 4.090e+00, 1.000e+00, 2.960e+02, 1.530e+01, 3.969e+02,\n",
       "        4.980e+00]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.data[0].reshape(1,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston.data[0].reshape(1,-1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rahul-raoniar/miniconda3/envs/datascience/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.41709233,  0.29216419, -1.27338003, -0.28154625, -0.16513629,\n",
       "         0.34715902, -0.13030059,  0.15267164, -0.97798895, -0.66777595,\n",
       "        -1.32142483,  0.42854113, -1.04769976]])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.transform(boston.data[0].reshape(1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rahul-raoniar/miniconda3/envs/datascience/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([30.08649576])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regression.predict(scaler.transform(boston.data[0].reshape(1,-1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Picking the model for deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(regression, open(\"regmodel.pkl\", \"wb\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_model = pickle.load(open(\"regmodel.pkl\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rahul-raoniar/miniconda3/envs/datascience/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([30.08649576])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict by loading pickle file\n",
    "pickle_model.predict(scaler.transform(boston.data[0].reshape(1,-1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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